Deep Transfer Learning to Classify Mass and Calcification Pathologies from Screen Film Mammograms

Computer-aided Diagnosis of Screen Film Mammograms

Automated Mass Detection from Mammograms using Deep Learning and Random Forest

Mass detection from mammogram plays an crucial role as a pre-processing stage for mass segmentation and classification. In this paper, we present a novel approach for detecting masses from mammograms using a cascade of deep learning and random forest classifiers. The deep learning classifier consists of a multi-scale deep belief network classifier that selects regions to be further processed by...

Adversarial Deep Structured Nets for Mass Segmentation from Mammograms

Mass segmentation provides effective morphological features which are important for mass diagnosis. In this work, we propose a novel end-to-end network for mammographic mass segmentation which employs a fully convolutional network (FCN) to model a potential function, followed by a CRF to perform structured learning. Because the mass distribution varies greatly with pixel position, the FCN is co...

Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks

Mammography is the most widely used method to screen breast cancer. Because of its mostly manual nature, variability in mass appearance, and low signal-to-noise ratio, a significant number of breast masses are missed or misdiagnosed. In this work, we present how Convolutional Neural Networks can be used to directly classify pre-segmented breast masses in mammograms as benign or malignant, using...

The Automated Learning of Deep Features for Breast Mass Classification from Mammograms

The classification of breast masses from mammograms into benign or malignant has been commonly addressed with machine learning classifiers that use as input a large set of hand-crafted features, usually based on general geometrical and texture information. In this paper, we propose a novel deep learning method that automatically learns features based directly on the optmisation of breast mass c...